Analysis of Deep Learning Action Recognition for Basketball Shot Type Identification

Carlos Olea, Gus Omer, John Carter, Jules White

2021

Abstract

Recent technologies have been developed to track basketball shooting and provide detailed data on shooting accuracy for players. Further segmenting this shot accuracy data based on shot types allows a more detailed analysis of player performance. Currently this segmentation must be performed manually. In this paper, we apply a state-of-the-art action recognition model to the problem of automated shot type classification from videos. The paper presents experiments performed to optimize shot type recognition, a unique taxonomy for the labeling of shot types, and discusses key results on the task of categorizing three different shot types. Additionally, we outline key challenges we uncovered applying current deep learning techniques to the task of shot type classification. The NOAH system enables the capture of basketball shooting data by recording every shot taken on a court along with its shooter and critical statistics. We utilized videos of 50,000 practice shots from various players captured through NOAH system to perform the task of classifying shot type. These three second video clips contain the shooting action along with movements immediately preceding and following the shot. On the problem of shot type classification, the Temporal Relational Network achieved an accuracy of 96.8% on 1500 novel shots.

Paper Citation

in Harvard Style

Olea C., Omer G., Carter J. and White J. (2021). Analysis of Deep Learning Action Recognition for Basketball Shot Type Identification. In Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS, ISBN 978-989-758-539-5, pages 19-27. DOI: 10.5220/0010650200003059

in Bibtex Style

@conference{icsports21,
author={Carlos Olea and Gus Omer and John Carter and Jules White},
title={Analysis of Deep Learning Action Recognition for Basketball Shot Type Identification},
booktitle={Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,},
year={2021},
pages={19-27},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010650200003059},
isbn={978-989-758-539-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS,
TI - Analysis of Deep Learning Action Recognition for Basketball Shot Type Identification
SN - 978-989-758-539-5
AU - Olea C.
AU - Omer G.
AU - Carter J.
AU - White J.
PY - 2021
SP - 19
EP - 27
DO - 10.5220/0010650200003059